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Model: goasty/Qwen3-4B-Indian-Law Source: Original Platform
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README.md
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README.md
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---
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license: apache-2.0
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language:
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- en
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tags:
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- legal
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- law
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- indian-law
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- legal-assistant
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- qwen3
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- unsloth
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- lora
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- instruction-tuning
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- question-answering
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- legal-reasoning
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datasets:
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- Prarabdha/indian-legal-supervised-fine-tuning-data
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- viber1/indian-law-dataset
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base_model:
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- unsloth/Qwen3-4B
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pipeline_tag: text-generation
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library_name: transformers
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---
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# Qwen3-4B Indian Law
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A domain-adapted legal assistant fine-tuned from **Qwen3-4B** on a large corpus of Indian legal texts, statutory provisions, constitutional law, criminal law, evidence law, procedural law, and court judgments.
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The model is designed to assist with:
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- Indian legal question answering
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- Statutory interpretation
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- Constitution-related queries
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- Criminal law and procedure
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- Legal reasoning
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- Case law understanding
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- Legal research assistance
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- Judgment summarization
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- Legal education and training
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---
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# Model Overview
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| Item | Value |
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|--------|--------|
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| Base Model | unsloth/Qwen3-4B |
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| Fine-Tuning Method | LoRA + QLoRA |
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| Framework | Unsloth |
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| Context Length | 4096 |
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| Precision | BF16 |
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| LoRA Rank | 32 |
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| LoRA Alpha | 32 |
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| Optimizer | AdamW 8-bit |
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| Learning Rate | 2e-4 |
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| Scheduler | Cosine |
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| Epochs | 2 |
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| Effective Batch Size | 32 |
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| Domain | Indian Legal Knowledge |
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---
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# Training Dataset
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The training corpus was created by combining multiple publicly available Indian legal datasets together with a large judgment corpus.
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The objective was to expose the model to:
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- Legal question answering
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- Statutory provisions
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- Constitutional law
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- Criminal law
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- Procedural law
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- Evidence law
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- Court judgments
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- Legal summarization
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- Legal reasoning
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---
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# Dataset Composition
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## 1. Indian Legal Supervised Fine-Tuning Dataset
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Source:
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```text
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Prarabdha/indian-legal-supervised-fine-tuning-data
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```
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Characteristics:
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- Large-scale legal instruction dataset
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- Context → Question → Answer format
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- Derived from Indian court judgments
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- Designed for legal reasoning and legal QA
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Original Size:
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```text
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6,055,371 samples
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```
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To prevent over-representation and memorization, a subset was selected during dataset balancing.
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Contribution:
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```text
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≈ 250,000 samples
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```
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Example:
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```text
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Context:
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Delhi Development Authority v. Kanwar Kumar Mehta
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Question:
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Was the High Court justified in calculating interest on escalation charges?
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Answer:
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Yes. The High Court's decision was held justified on equitable grounds.
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```
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---
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## 2. Indian Law Instruction Dataset
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Source:
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```text
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viber1/indian-law-dataset
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```
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Characteristics:
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- Legal instruction-response pairs
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- Covers Indian legal concepts
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- General legal knowledge
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- Legal terminology
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Samples:
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```text
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24,607
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```
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Example:
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```text
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Question:
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What is the difference between a petition and a plaint?
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Answer:
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A petition is a formal request seeking relief, whereas a plaint is the written statement initiating a civil suit.
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```
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---
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## 3. Constitution of India QA Dataset
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Custom processed dataset containing question-answer pairs generated from constitutional provisions.
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Coverage:
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- Fundamental Rights
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- Directive Principles
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- Union and State relations
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- Parliament
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- Judiciary
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- Constitutional amendments
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|
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Samples:
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|
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```text
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4,082
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```
|
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|
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Example:
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|
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```text
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Question:
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What is India according to the Constitution?
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Answer:
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India, that is Bharat, shall be a Union of States.
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```
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---
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## 4. Indian Penal Code (IPC) Dataset
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Custom processed IPC question-answer corpus.
|
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|
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Coverage:
|
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|
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- Definitions
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- Offences
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- Punishments
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- Criminal liability
|
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- General exceptions
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|
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Samples:
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|
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```text
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2,267
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```
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Example:
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```text
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Question:
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What is the title and extent of operation of the Indian Penal Code?
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Answer:
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The title is the Indian Penal Code and it extends to offences committed within India and certain offences committed outside India.
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```
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---
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## 5. Code of Criminal Procedure (CrPC) Dataset
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Custom processed question-answer dataset generated from CrPC provisions.
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Coverage:
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- Investigation
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- Arrest
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- Bail
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- Trial procedures
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- Appeals
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- Criminal courts
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|
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Samples:
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|
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```text
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8,194
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```
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Example:
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```text
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Question:
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What is the short title and commencement of the CrPC?
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Answer:
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The Code of Criminal Procedure, 1973.
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```
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---
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## 6. IndicLegalQA
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Legal question-answer dataset derived from Indian Supreme Court judgments.
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|
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Coverage:
|
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|
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- Case law
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- Judicial reasoning
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- Legal interpretation
|
||||
|
||||
Samples:
|
||||
|
||||
```text
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||||
10,002
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||||
```
|
||||
|
||||
Example:
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||||
|
||||
```text
|
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Question:
|
||||
Who was the respondent in Union of India?
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Answer:
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Maj. Gen. Manomoy Ganguly.
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```
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||||
---
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## 7. Bharatiya Nyaya Sanhita (BNS)
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Structured dataset generated from the Bharatiya Nyaya Sanhita, 2023.
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|
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Coverage:
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|
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- Criminal offences
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- Punishments
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- Definitions
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- Modern criminal law provisions
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|
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Source Structure:
|
||||
|
||||
```text
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||||
Chapter
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||||
Section
|
||||
Section Name
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||||
Description
|
||||
```
|
||||
|
||||
---
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||||
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## 8. Bharatiya Sakshya Adhiniyam (BSA)
|
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|
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Structured dataset generated from the Bharatiya Sakshya Adhiniyam, 2023.
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|
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Coverage:
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|
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- Evidence law
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- Documentary evidence
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- Digital evidence
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- Witness testimony
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|
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Source Structure:
|
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|
||||
```text
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||||
Chapter
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||||
Section
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||||
Section Name
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||||
Description
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||||
```
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|
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---
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## 9. Indian Court Judgments Corpus
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|
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Largest component of the training data.
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|
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Sources include:
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|
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- Supreme Court judgments
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- High Court judgments
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- CourtNIC archives
|
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- JUDIS archives
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|
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Documents processed:
|
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|
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```text
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16,726 judgment files
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```
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Coverage:
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|
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- Constitutional law
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- Civil law
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- Criminal law
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- Taxation
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- Property law
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- Administrative law
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- Service law
|
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|
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Training samples were automatically converted into:
|
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|
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```text
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Context → Question → Answer
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```
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instruction format.
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---
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# Dataset Balancing
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The original corpus was heavily dominated by judgment-derived samples.
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Without balancing:
|
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|
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```text
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451,756 samples
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```
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Distribution:
|
||||
|
||||
```text
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Judgment-heavy
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```
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|
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To improve generalization across statutory and constitutional law, a balancing procedure was applied.
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Final balanced dataset:
|
||||
|
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```text
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304,930 samples
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```
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Approximate distribution:
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| Category | Samples |
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|-----------|-----------|
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| General Legal QA | 190,744 |
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| Court Judgments | 66,368 |
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| Constitution | 32,346 |
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| CrPC | 8,719 |
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| IPC | 6,698 |
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| BNS | 50 |
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| BSA | 5 |
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This balancing significantly reduced bias toward judgment memorization while preserving broad legal coverage.
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---
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# Training Configuration
|
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The model was fine-tuned using LoRA adapters on top of Qwen3-4B.
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## LoRA Configuration
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```python
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r=32
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lora_alpha=32
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lora_dropout=0.0
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```
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Target Modules:
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```python
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q_proj
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k_proj
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v_proj
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o_proj
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||||
gate_proj
|
||||
up_proj
|
||||
down_proj
|
||||
```
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---
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## Optimization
|
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|
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```python
|
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Learning Rate: 2e-4
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Weight Decay: 0.01
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Warmup Ratio: 0.03
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||||
Scheduler: Cosine
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||||
Optimizer: AdamW 8-bit
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||||
```
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||||
|
||||
---
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||||
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||||
## Training
|
||||
|
||||
```python
|
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Epochs: 2
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Max Sequence Length: 4096
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Batch Size: 8
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Gradient Accumulation: 4
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Effective Batch Size: 32
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Precision: BF16
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```
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---
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# Example Usage
|
||||
|
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```python
|
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from transformers import AutoTokenizer, AutoModelForCausalLM
|
||||
|
||||
model_name = "goasty/Qwen3-4B-Indian-Law"
|
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|
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tokenizer = AutoTokenizer.from_pretrained(model_name)
|
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|
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model = AutoModelForCausalLM.from_pretrained(
|
||||
model_name,
|
||||
torch_dtype="auto",
|
||||
device_map="auto"
|
||||
)
|
||||
|
||||
prompt = """
|
||||
What is Article 21 of the Constitution of India?
|
||||
"""
|
||||
|
||||
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
|
||||
|
||||
outputs = model.generate(
|
||||
**inputs,
|
||||
max_new_tokens=256
|
||||
)
|
||||
|
||||
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
# Intended Use
|
||||
|
||||
Suitable for:
|
||||
|
||||
- Legal research assistance
|
||||
- Educational purposes
|
||||
- Law students
|
||||
- Legal document analysis
|
||||
- Statutory interpretation
|
||||
- Legal Q&A systems
|
||||
- Retrieval-Augmented Generation (RAG)
|
||||
|
||||
---
|
||||
|
||||
# Limitations
|
||||
|
||||
- Not a substitute for licensed legal counsel.
|
||||
- May generate legally incorrect or outdated interpretations.
|
||||
- Should not be relied upon for litigation or legal advice without expert review.
|
||||
- Training data contains historical judgments and statutes which may have been amended or overruled.
|
||||
|
||||
---
|
||||
|
||||
# Acknowledgements
|
||||
|
||||
This work builds upon:
|
||||
|
||||
- Qwen Team
|
||||
- Unsloth
|
||||
- Hugging Face Datasets Community
|
||||
- Indian Legal Open Data Contributors
|
||||
- Supreme Court and High Court public legal records
|
||||
|
||||
---
|
||||
|
||||
# Citation
|
||||
|
||||
```bibtex
|
||||
@misc{qwen3_indian_law,
|
||||
title={Qwen3-4B Indian Law},
|
||||
author={Aditya},
|
||||
year={2026},
|
||||
note={Fine-tuned Qwen3-4B model for Indian legal reasoning and question answering}
|
||||
}
|
||||
```
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added_tokens.json
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added_tokens.json
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{
|
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"</think>": 151668,
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|
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|
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|
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"<tool_response>": 151665,
|
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"<|box_end|>": 151649,
|
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"<|box_start|>": 151648,
|
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"<|endoftext|>": 151643,
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|
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|
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"<|fim_pad|>": 151662,
|
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|
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"<|fim_suffix|>": 151661,
|
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"<|im_end|>": 151645,
|
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"<|im_start|>": 151644,
|
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"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
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"<|object_ref_start|>": 151646,
|
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"<|quad_end|>": 151651,
|
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"<|quad_start|>": 151650,
|
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"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
97
chat_template.jinja
Normal file
97
chat_template.jinja
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|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
||||
{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
||||
{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for forward_message in messages %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- set message = messages[index] %}
|
||||
{%- set tool_start = '<tool_response>' %}
|
||||
{%- set tool_start_length = tool_start|length %}
|
||||
{%- set start_of_message = message.content[:tool_start_length] %}
|
||||
{%- set tool_end = '</tool_response>' %}
|
||||
{%- set tool_end_length = tool_end|length %}
|
||||
{%- set start_pos = (message.content|length) - tool_end_length %}
|
||||
{%- if start_pos < 0 %}
|
||||
{%- set start_pos = 0 %}
|
||||
{%- endif %}
|
||||
{%- set end_of_message = message.content[start_pos:] %}
|
||||
{%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set content = message.content %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is defined and message.reasoning_content is not none %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in message.content %}
|
||||
{%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
|
||||
{%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
|
||||
{%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- message.content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
|
||||
{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
70
config.json
Normal file
70
config.json
Normal file
@@ -0,0 +1,70 @@
|
||||
{
|
||||
"architectures": [
|
||||
"Qwen3ForCausalLM"
|
||||
],
|
||||
"attention_bias": false,
|
||||
"attention_dropout": 0.0,
|
||||
"eos_token_id": 151645,
|
||||
"head_dim": 128,
|
||||
"hidden_act": "silu",
|
||||
"hidden_size": 2560,
|
||||
"initializer_range": 0.02,
|
||||
"intermediate_size": 9728,
|
||||
"layer_types": [
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention",
|
||||
"full_attention"
|
||||
],
|
||||
"max_position_embeddings": 40960,
|
||||
"max_window_layers": 36,
|
||||
"model_type": "qwen3",
|
||||
"num_attention_heads": 32,
|
||||
"num_hidden_layers": 36,
|
||||
"num_key_value_heads": 8,
|
||||
"pad_token_id": 151654,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 1000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"torch_dtype": "bfloat16",
|
||||
"transformers_version": "4.53.3",
|
||||
"unsloth_fixed": true,
|
||||
"unsloth_version": "2025.8.1",
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"max_length": 40960,
|
||||
"pad_token_id": 151654,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"transformers_version": "4.53.3"
|
||||
}
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5074f9bc4a5254c4ae7a04ad6900c87e43abea17709a3ec72922bded37e0a2e6
|
||||
size 4967215360
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:8e67025a7a0bc95f6302c352f6d7dda020296d1da84a5690afea21ec88fc6f00
|
||||
size 3077766632
|
||||
405
model.safetensors.index.json
Normal file
405
model.safetensors.index.json
Normal file
@@ -0,0 +1,405 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_size": 8044936192
|
||||
},
|
||||
"weight_map": {
|
||||
"model.embed_tokens.weight": "model-00001-of-00002.safetensors",
|
||||
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|
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
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|
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|
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|
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|
||||
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|
||||
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|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
240
tokenizer_config.json
Normal file
240
tokenizer_config.json
Normal file
@@ -0,0 +1,240 @@
|
||||
{
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151654": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151656": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
},
|
||||
"151659": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151662": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151663": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151664": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"151668": {
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 40960,
|
||||
"pad_token": "<|vision_pad|>",
|
||||
"padding_side": "right",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user